ASME Conference Presenter Attendance Policy and Archival Proceedings

This online compilation of papers from the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2016) represents the archival version of the Conference Proceedings. According to ASME’s conference presenter attendance policy, if a paper is not presented at the Conference by an author of the paper, the paper will not be published in the official archival Proceedings, which are registered with the Library of Congress and are submitted for abstracting and indexing. The paper also will not be published in The ASME Digital Collection and may not be cited as a published paper.

Due to the effect of the antenna plate flatness on the antenna performances, the flatness is one of the key performance indicators for the planar antenna. Before calculating the antenna plate flatness, the support assembly tools are built, and then measuring experiment for height coordinate values is carrying out on the assembly platform. This paper presents a predictive method that is the Radial Basis Function (RBF) neural network method to obtain the height coordinate values based on fewer measurement points on the antenna plate after welding assembly, and the antenna plate flatness is calculated by fitting least square plane using measuring point coordinate value through the least square method (LSM). Simultaneously, before or after welding assembly, comparing with the calculated flatness value, it is shown that the calculated flatness value by the predicted height coordinate values basically agrees well with the initial calculated flatness value. These results reveal that the RBF neural network prediction is adopted to be very correct and valid, which will reduce the measurement cost and improve measurement efficiency in future.

Industrial symbiosis can be understood as the substitution of new resources used in an industrial process by another resource that would otherwise be discarded. Industrial symbiosis can thereby create new revenue streams and at the same time reduce environmental impact. The initial step in creating an industrial symbiosis is the identification of potential substation relationships between production plants. This step is challenging, as information about the companies is often not available. Several software tools have been developed in order to identify potential symbiosis opportunities. However, these tools have the shortcoming that they require extensive data input from companies owning the production plants. This requirement limits the number of companies for which symbiosis opportunities are identified. In this paper, we propose a data-driven methodology for identifying industrial symbiosis and generating eco-industrial park architectures. The methodology is based on meta-models of industrial plants for identifying plant attributes for certain types of plants, correlations that estimate the rough amount of resource supply and demand of a plant, and a rule-based system that identifies symbiosis opportunities based on knowledge from successful symbioses. Based on the symbiosis opportunities, approach generates eco-industrial park architectures that are optimal in terms of economic and environmental performance. Finally, we apply the methodology to a case study of the existing Kalundborg eco-industrial park to evaluate if the methodology is capable of finding existing symbioses. We conclude that the methodology can be applied to screening industrial zones with standard types of industrial plants. However, the results depend on the types of existing industrial plant meta-models in the database. Future work will focus on extending the data and knowledge base; and validating the methodology by its application to other existing eco-industrial parks.

Artificial Neural Networks (ANNs) have been used to predict assembly time and market value from assembly models. This was done by converting the assembly models into bipartite graphs and extracting 29 graph complexity metrics which were used to train the ANN prediction models. This paper presents the use of sub-assembly models instead of the entire assembly model to predict assembly quality defects at an automotive OEM. The size of the training set, order of the bipartite graph, selection of training set, and defect type were experimentally studied. With a training size of 28 parts, an interpolation focused training set selection, and second order graph seeding, over 70% of the predictions were within 100% of the target value. The study shows that with an increase in training size and careful selection of training sets, assembly defects can be predicted reliably from sub-assemblies complexity data.

The once ubiquitous national space program, characterized by large-scale experiments, nearly gratuitous levels of funding, and unrivaled access to materials and talent, has given way to a multitude of smaller, decentralized entities. Furthermore, it is likely that a large market will soon exist for space tourism, suborbital passenger delivery, and other low payload missions. This trend has resulted in the potential for a substantial technological shift away from traditionally used large-scale rocket engines to smaller scale, reusable engines. A rocket that is liquid-fueled is ideal for this context, because liquid fueled engines are generally regarded as being safer than solid fuel engines, and unlike hybrid rockets, they can be refueled. However, the downsizing of liquid fueled rocket engines has not been thoroughly explored. Subsequently, there are many engineering challenges that have yet to be solved. Despite this difficulty, the vacuum of knowledge also presents an opportunity to pursue innovative designs and manufacturing techniques that are untenable for larger engines. This work seeks to overcome said obstacles and implement creative solutions to pursue small-scale rocket technology. Specifically addressed by this research is the design of the injector system and necessary considerations to facilitate manufacturing.

In order to improve the balance and load equilibrium of aircraft assembly lines, and to enhance the management of on-site resources, a Type-E balancing method was proposed based on the mobile operation of assembly personnel in the aircraft assembly line. This method was aimed to minimize the smoothness index of the overall assembly line and each assembly station, and also to minimize manpower costs. First, a model of personnel flow and an assembly line balancing model were constructed based on the characteristics of aircraft assembly lines. Next, an Accelerated Binary Particle Swarm Optimization (ABPSO) based on improved sig function was designed in order to improve the original stability and convergence of the standard binary particle swarm algorithm. Finally, the validity of the method was verified with a real fuselage assembly line and the results show the effectiveness.

The objective of this research is to study the improvement in the formability of thermoplastics using heat assisted single point incremental forming. Single point incremental forming is a production process for forming sheet materials without the use of dedicated tooling (dies/molds). The process is an alternative to thermoforming for low volume forming of sheets. It involves forming the final shape through a series of localized incremental deformations. It has been observed that heat assisted techniques have shown an improvement in the formability limits for sheet metals. In this research, this concept has been tested for improving the formability of polymer sheets. Hot air us used to increase the temperature within a localized region in front of the tool. A single point incremental forming device is modified through the development of a specialized tool holder and nozzle which heats the polymer sheet to temperatures higher than the room temperature but below the glass transition temperature of the polymer and applies the forming loads.

The results from the experiments are summarized as: i) the formability angle increases of polystyrene from 27 degrees to 46 degrees when comparing room temperature forming to forming at an elevated temperature (170°F–180 °F), ii) a reduction in the forces needed for forming is observed qualitatively, and iii) the surface finish on the formed parts do not show visible change. This demonstrates promise of manufacturing complex shapes from thermoplastic polymer sheets using heat assisted incremental forming. Future research includes 1) simulating the localized deformation of the material to enable process planning, 2) quantifying the forming forces and heat control of the system, and 3) exploring the manufacturing technique to other materials.

This work proposes the application of a recently developed locomotion system to material handling for a flexible manufacturing layout. The “swing-and-dock” (SaD) model for realizing displacements has been invented for and is used by the mobile robotic fixtures developed in the SwarmItFix European project. This form of locomotion can be a valuable capability for material handling agents, enabling simultaneous handling in a non-linear fashion and increasing manufacturing flexibility. The paper focuses on the design of SaD path planning algorithms for the motion of a single agent. Several possible strategies and solutions are presented, elaborated, and tested via simulations. The results suggest that a Nearest Neighbor with a Random Insert Heuristic approach allows the generation of good solution sequences for a single agent with SaD locomotion visiting a varied size of destinations with stationary obstacles in its path.

The fatigue life of a component is defined as the total number of cycles or time to induce fatigue damage and to initiate a dominant fatigue flaw which is propagated to final failure.(Shigley & Mischke 2002)

The aim of this project is to calculate the total fatigue life of metallic structures under cyclic loading by applying equations found by Basquin and Manson-Coffin. The local stresses and strains necessary for the calculation are determined by the finite element method. Former studies concerning this subject have used analytical methods to find the local conditions at the critical section. The analytical methods, based on Neuber and Molski-Glinka’s approaches, permit the calculation of the local stresses and strains at the critical section of the structure’s geometry as a function of the nominal stress (forces) applied.

For the finite elements method, ABAQUS is used to determine the local conditions at the critical section of a T-shaped model.

One of the primary challenges faced in Additive Manufacturing (AM) is reducing the overall cost and printing time. A critical factor in cost and time reduction is post-processing of 3D printed (3DP) parts, of which removing support structures is one of the most time consuming steps. Support is needed to prevent the collapse of the part or certain areas under its own weight during the 3D printing process. Currently, the design of self-supported 3DP parts follows a set of empirical guide lines. A trial and error process is needed to produce high quality parts by Fused Depositing Modeling (FDM). The usage of chamfer angle with a max 45° angle form the horizontal for FDM is a common example. Inclined surfaces with a smaller angle are prone to defects, however no theoretical basis has been fully defined, therefore a numerical model is needed. The model can predict the problematic areas at a print, reducing the experimental prints and providing a higher number of usable parts.

Physical-based models have not been established due to the generally unknown properties of the material during the AM process. With simulations it is possible to simulate the part at different temperatures with a variety of other parameters that have influence on the behavior of the model. In this research, analytic calculations and physical tests are carried out to determine the material properties of the thermoplastic polymer Acrylonitrile - Butadiene - Styrene (ABS) f or FDM at the time of extrusion. This means that the ABS is going to be extruded at 200°C to 245°C and is a viscous material during part construction.

Using the results from the physical and analytical models, i.e., Timoshenko’s modified beam theory for micro-structures, a numerical material model is established to simulate the filament deformation once it is deposited onto the part. Experiments were also used to find the threshold for different geometric specifications, which could then be applied to the numerical model to improve the accuracy of the simulation. The result of the finite element analysis is compared to experiments to show the correlation between the prediction of deflection in simulation and the actual deflection measured in physical experiments.

A case study was conducted using an application that optimizes topology of complex geometries. After modeling and simulating the optimized part, areas of defect and errors were determined in the simulation, then verified and and measured with actual 3D prints.

21st Design for Manufacturing and the Life Cycle Conference: Design for Quality, Reliability, and Cost

This paper presents the results of teaching robust design techniques to industrial design students in the Chinese context. Year 3 students in a Bachelor of Engineering programme at Xi’an Jiaotong-Liverpool University in Suzhou, China studied robust design techniques over a one-semester course. As part of the course, the students worked in two groups to apply robust design techniques to traditional Chinese musical instruments: the jinghu and the erhu. The two groups took divergent approaches to the project. One group sought to follow traditional manufacturing processes (craftmaking) for the instruments, and the other applied typical simple prototyping techniques used within industrial design. Through selection of control factors, creation of orthogonal arrays, prototyping, and experimentation, the students were able to characterize the main effects of four control factors on the sound quality of the instruments, loudness or harmonics, and to explore the feasibility of robust design for instrument design.

Circular economy is largely recognized as the univocal economic model that guarantees a long-term sustainability, decoupling the economic growth and the finite resources consumption. As a prerequisite, it requires to realize product closed-loop lifecycles. However, the management of the EoL phase during the design process is a complex task, due to the fact that it is the most far away phase, in terms of time, from the moment of the product conception. For this reason, usually, manufacturers and EoL stakeholders do not actively collaborate in optimizing the product lifecycle performances.

This paper wants to overcome this lack proposing a method to formalize, collect and classify the EoL knowledge. The main outcome is a structured database containing positive and negative knowledge about best practices and disassembly problems faced during dismantling activities. The knowledge classification rules are based both on product characteristics (e.g. product families, target components, assembly methods, etc.) and on other more general aspects (e.g. motivations of the disassembly, handling difficulties, etc.). Through the sharing of this knowledge, the gap between design departments and EoL stakeholders can be reduced with the aim to improve EoL performances and the overall resource efficiency.

This work is focused on an out-of-service washing machine case study. The product has been manually disassembled by expert operators, observing and rating the significant problems. Their interpretation has allowed to create a set of specific design guidelines, organized according to the defined rules. The classified knowledge has been used by non-expert designers (undergraduate students) as a tool to guide the re-design activities. Different design solutions (e.g. homogenization of screws, reduction of component number, etc.) have been implemented to configure a new washing machine version, improved from the disassemblability point of view. The obtained results have confirmed the usefulness of the disassembly knowledge sharing in supporting Design for EoL activities and, furthermore, in non-skilled operators training.

In conclusion, this research work contributes to the state of the art linking stakeholders involved in the Beginning of Life (BoL) with stakeholders responsible of the EoL management. Furthermore, the proposed work leads to relevant improvements in product lifecycle performances. The proposed knowledge database represents the needed resource to effectively extend the producer responsibility and to close the current gap between manufacturers and dismantlers.

Reliability analysis is particularly relevant for industrial plants where plant failures can lead to large financial losses. Existing reliability analysis approaches mostly rely on heavy-weight simulations that are computationally expensive and require extensive modeling effort. On the other hand, there is an industrial need for quickly evaluating plant reliability for developing new services and business models. In this paper, we extend and apply the reliability bound approach using linear programming to address this need. The reliability bound approach is based on a system model in the form of a graph, an event vector, and estimates for component reliabilities. Based on this model, lower and upper reliability bounds are calculated by solving a linear programming problem. The advantage of this approach is the ubiquity of solvers for linear programming. Furthermore, the approach is guaranteed to produce the narrowest bound with respect to the reliability data. We demonstrate the applicability of the approach to a subsystem of an industrial plant as a test case. Future work consists applying the method to whole plants and comparing the results with simulation-based approaches. Moreover, the approach is planned to be extended to system attributes such as buffers and multiple failure states.

Intrathecal (IT) drug delivery is a preferred treatment for chronic pain, brain cancers and spasticity. However, the application of IT drug delivery treatment is still limited by the large patient-to-patient variations and numerous kinds of rare genetic diseases. A fast, relatively cheap and subject-specific in-vitro method to study the drug bio-dispersion mechanism and optimize the intrathecal drug therapies for individual patients is in great need. In this study, we will investigate the model design and additive manufacturing process for producing a subject-specific spine model, which will simulate the interaction of the real human spine with cerebrospinal fluid (CSF). Research issues including watertight 3D printable model construction and 3D printing of anatomical accurate, physiological functional spine models are discussed in this paper. A pipeline of additive manufacturing in-vitro subject-specific models for study of cerebrospinal fluid and drug transport in spine is presented.

High tensile strength cables, low-resistance motor windings, and shape memory actuators are common examples of technical fibers used in robots and other electromechanical assemblies. Because properties like tensile strength, crystal structure, and polymer alignment depend strongly on processing history, these materials cannot be 3D printed with the same properties they have on the spool. Strings and fibers are inserted in mechanical parts at the end of the manufacturing process for these assemblies. When the fibers take complex paths, the installation is often done by hand. This activity can dominate the process time, increase its human labor and reduce its social sustainability [1].

This paper applies the non-traditional approach of machine embroidery to insert sheets of patterned fibers in layered additive manufacturing processes such as 3D printing and lamination. Fibers are aligned with features in laser-cut or printed parts without the manual labor of hand threading. We demonstrate that water-soluble stabilizer materials originally designed for textiles can hold hard mechanical parts in a machine embroidery hoop with enough strength and rigidity to withstand sewing through pre-existing holes in the part. Alignment to within 250 microns has been demonstrated with a sub-$300 consumer embroidery machine.

Case studies in this paper include a cable-driven mechanism, a soft-to-hard electronic connection, and an electromechanical sensor. Process-compatible and commercially available materials that can be embroidered include conductive threads, shrinking threads, water-soluble threads and high tensile strength fibers.

The biggest hurdle for a user interested in this automated fiber installation process is linking the existing design file with an embroidery machine file. There is a much larger user base for 2D and 3D computer-assisted design (CAD) software than for expensive and proprietary embroidery digitizing software. We take the route chosen by the laser cutter industry, where the user produces a CAD file in their preferred editor, and makes annotations that communicate where and how densely to stitch. Translation software scans the file for a particular line style and generates stitch coordinates along it. Development is done in Jupyter/iPython notebooks that allow end-users to inspect, understand, and modify the conversion code. The intent is for users of existing planar fabrication technology (whether laser, printed circuit board, or micro/nano) to apply this method to their own CAD files for a versatile and straightforward way to put advanced materials in their devices without adding manual labor. This general approach can solve a class of assembly problems relevant to underactuated tendon-driven robotics and other electromechanical systems, expanding the range of devices that can be put together using automation.

In design for assembly (DFA), minimizing part numbers is a significant guideline for product design improvement. For this reason, producing a single product regardless of its geometric complexity has been noted as one of the key advantages of additive manufacturing (AM) in the literature. This paper, however, points out that designing assembly parts also has positive influences on AM productivity. In fact, assembly designs enable AM manufacturers to reduce the build time by lowering the height of parts, to decrease the material cost by reducing the amount of support, and to improve the surface quality by deciding the proper orientations for build-up parts. For applying to AM, we propose the assembly design procedure that includes two main phases: designing assembly parts employing design guidelines and evaluating the assembly parts with a quantitative method. In the guidelines for assembly designs, there are mainly two categories: (1) basic principles for assembly designs; and (2) connection types for part assemblies. The former deals with how we separate a single product into several parts and how we make a decision about a build-up orientation in order to improve the AM productivity. The latter covers connection methods that explain how divided parts are connected into a single product again. To assess the assembly part designs quantitatively, we suggest a simple estimation model based on the digital light processing (DLP), an AM technology that results in high efficiency in terms of the build time. A numerical example is provided to show how different assembly designs are compared to select the best one with the highest AM productivity.

3D printing technologies have been widely used in Tissue Engineering. The pneumatic dispensing system is one of the promising dispensing technologies to fabricate scaffolds with controllable pore sizes and porosities. There are many factors to affect the printing quality in pneumatic systems. An effective method is required to help users to systematically select proper operational parameters to print strands with desired width. In this paper, the DoE method is introduced to evaluate operational parameters and their interactions. The solution is further verified using regression tests. Based on the proposed solution, a five steps parameters selection method is proposed and verified to select operational parameters for two targeted strand widths.

With the increasing attention on the role of consumer behavior in sustainable development, consideration of consumer’s product repair and reuse behavior is becoming more and more important in the product design domain. In order to investigate the product ease-of-repair and its effect on future product purchase and recommendation decisions made by consumers, this paper studies the main reasons that consumers were not able to repair a product based on a survey data collected by a wiki-based website that offers repair manuals for consumer electronics. Two main questions have been asked in the survey: what is the last thing you personally fixed? And why did you not succeed in fixing it. The information of these questions and the available response options have been used to compared eleven types of electronics in terms of their ease-of-repair. A list of design features (e.g. openability, accessibility, standardization, and modularity) that may increase the repair adoption by individual consumers has been discussed. In addition, a Data Envelopment Analysis (DEA) method was introduced to compare different categories of products in terms of reparability efficiency. The findings on how repair experiences or efficiency of repair for different categories of devices influence consumers’ future purchase and recommendation decisions have been presented.

Shape deviation is one of the major issues in fabricating a part using Additive Manufacturing (AM) technology. During the prototype fabrication process, slicing induced conditions play a significant role in varying shape deviations. This paper provides a novel technique for the estimation and simulation of shape deviation and provides a foundation to estimate the combined effect of oversize and undersize fabrication conditions on the same layer. The research described is the initial efforts to develop a shape deviation model regarding oversize, undersize, equal size and mixed size situations. To estimate and simulate the shape deviation, an algorithm is developed and examined with seven example tessellated prototypes. The advanced program will help the user to determine the different shape deviation information during slicing stage with different preprocessing parameters. The results show that the developed methodology is robust and work as an efficient alternative to shape deviation studies.

An efficient and intersection-free model offsetting framework is introduced in this paper to generate shell models for 3D printing. The basic concept of the framework is to offset vertices of the input mesh to obtain an approximate discrete signed distance field for reconstructing the offsetting mesh. The framework first offsets vertices of the mesh by a given distance along their normal directly. These vertices are then adjusted or discarded according to the given offsetting distance to form an approximate discrete signed distance field using a binary space partition (BSP) tree. These reserved vertices are finally reconstructed using Poisson reconstruction algorithms to form the inner surface of the shell model. Results of the framework are intersection and non-manifold free for an arbitrary distance. It also allows different parts of a model for different offsetting distances from user interactions. Several examples are given to demonstrate that the framework is effective and robust for 3D printing.

21st Design for Manufacturing and the Life Cycle Conference: Design of Sustainable Energy Systems

As sustainable building mandates become more prevalent in new commercial buildings, it is a challenge to create a broad, one-size-fits-all certification process. While designers can estimate energy usage with computation tools such as model based design, anticipating the post occupancy usage is more difficult. Understanding energy usage trends is especially complicated in university student housing buildings, where occupancy varies significantly as a function of enrollment and course scheduling. This research explores the effect of student occupancy on both predicted and actual energy usage in a LEED (Leadership in Energy and Environmental Design) Platinum certified student housing complex. A case study is presented from the California State University Fullerton (CSUF) campus, and examines diversity factor, defined as a building’s instantaneous energy usage as a percentage of the maximum allowable usage during a period of time, trends throughout the academic year. The CSUF case diversity factor is compared to the diversity factor used in predictive models for obtaining LEED certification, and the mandates that govern the models (e.g., ASHRAE 90.1). The results of the analysis show the benefits of considering post occupancy usage in sustainable building designs, and recommendations are presented for creating unique and application based computational models, early in the design process. This research has broad applications, and can extend to sustainable building design in other organizations, whose operational schedule falls outside of current prediction methods for sustainability mandates.

Energy use optimization of systems is traditionally done by looking for the best thermodynamic operating conditions of the process and heat integration solutions such as networks to redistribute intelligently the heat between different sources and sinks. Hence, there are two emerging situations in industrial parks that call for new methodologies: first, the flow exchanges are not limited to the plant boundaries, and incoming and outgoing flows could generate economic and environmental gains. Second, material such as waste can no longer be neglected in this optimization. Not only the energy conversion potential of material could be investigated, but other conversion options would bring the possibility of turning the non-usable waste into another usable material through chemical processes. This paper present a conceptual framework of optimization for an energy and mass system, and proposes a methodology for integrating conversion processes, with a case demonstration on wooden waste in an industrial activity zone.

Design and implementation of Eco-Industrial Parks (EIPs) is a practical and scientific solution to achieve sustainable industries. Specifically, energy exchange networks can significantly contribute to the pollution reduction by recovering and sharing wasted heat generated in industrial processes. Despite this perceived fact, the existing research seems mainly looking for one to one relation and lacks in methods for modeling and optimizing multi-synergy symbioses that is essential for EIPs. This paper reviews main concepts in designing industrial symbioses and proposes an optimization model to exchange the residual energy between individual industries in an EIP. Using mathematical programming, the model decides the best set of connections between energy suppliers and users to minimize the total cost and maximize energy symbioses. The presented models analyze perspectives to potential symbioses for individual industries and EIP managers. A detailed discussion clarifies how these perspectives can affect the optimized symbioses. The model is validated using anonymized data of a real case. The result shows that various perspectives to the model provide different energy network topographies.

The magnetocaloric effect (MCE) is a magneto-thermodynamic phenomenon that heats and cools specific alloys through exposure to an alternating magnetic field. This phenomenon has the potential to create a temperature difference in a heat carrier mimicking a conventional vapor compression refrigeration cycle without harmful chemical byproducts. This research investigates the design of an experimental testing mechanism for identifying key interactions between design variables, while maximize temperature differential Key noise parameters (KNP). Fluid flow rate, magnetic field exposure time, and variations in heat exchanger configuration are explored. Understanding the significant interactions between these variables will lead to the design of a functional prototype that serves as a basis for future development in applications of the MCE for large-scale cooling systems. In this work, elemental gadolinium is used due to its high magnetic entropy change, and consequently high reversible temperature change when exposed to a magnetic field. An aqueous propylene glycol solution serves as the heat carrier based on its high heat capacity and basic pH level, reducing the possibility of degradation within the magnetocaloric material. The magnetic field is supplied by a grade N52 magnet with a magnetic field strength of approximately 0.9 Tesla. Based on this analysis, a concept stage design for experimentally maximizing the impact of the magnetocaloric effect is presented.

21st Design for Manufacturing and the Life Cycle Conference: Engineering for Global Development

Industrial accidents continue to happen despite rapid technological advancement and they are often caused by triggers similar to those of past accidents. If we turn our eyes to the world, especially to the emerging industrial players, we hear news about accidents caused by phenomena that have already caused similar accidents elsewhere.

Industries, as they emerge and grow over hundreds of years, learn their lessons throughout their histories and build rules, regulations, and common knowledge to avoid accidents. Each industry is probably well aware of accidents that took place in its own country, especially when the accident led to enforcement of a new law. Nevertheless, we hardly have any knowledge of accidents in foreign countries unless they were of huge sizes.

Japan had a national project of building a database of knowledge and lessons learned from past accidents. Failure Knowledge Database (FKDB) went on the Web in 2005. As of today it still attracts a large number of readers with its over 1,600 failure cases. Our research is targeted at making use of this FKDB by abstracting the knowledge, especially what triggered the accidents, and comparing the knowledge with functional and structural elements used in new designs.

Design Record Graph (DRG) is a graphical representation of the designer’s intension starting from the left with the product functional requirement which iteratively divides into sub-functions to reach a set of functional elements (FE). Each FE maps to a structural element (SE). Then the SEs iteratively combine to form assemblies and finally the product at the right end. A failure starts from one of the FE-SE pairs and propagates the DRG in both left and right directions to reach the two ends. The propagation leaves a trace of how the point of failure led to disabling the product.

For each failure case in FKDB, we identified the origin of failure, the FE-SE pair that started the accident. An FE is abstracted by a verb phrase and a set of noun phrases, and similarly an SE with some noun phrases. By limiting the phrases to use, similar concepts are described by the same abstracted phrases.

A new design has a number of FE-SE pairs and their propagations in the DRG to reach the two ends. The designer can then compare all propagations in the design, without the knowledge if any of them are dangerous, with those in FKDB that are known to have led to accidents.

We developed quantitative operators to evaluate the similarity between two traces. Our results offer a way of warning the designer about possible flaws in a new design similar with causes of past accidents that the designer has no idea about. Our method of preventing design failure can apply to other fields for novice planners in avoiding failure while still in the planning stage. We can further develop the use of knowledge into overseas countries by mapping the limited number of verb and noun phrases into foreign language.

This work discusses the modeling and optimization of a drip irrigation emitter for reducing activation pressure. Our model formulation focuses on analytically characterizing fluid-structure interactions in an existing 8 liters per hour (lph) pressure-compensating online emitter. A preliminary experimental validation of the resulting model was performed for three different emitter architectures. This model was used as a basis for a genetic algorithm-based optimization algorithm that focused on minimizing activation pressure. The design variables considered in our formulation include, geometric features of the emitter architecture, and practical constraints from manufacturing. We applied our optimization approach to four emitters (with flow rates of 4, 6, 7 and 8.2 lph) and were able to lower activation pressure by more than half in each case. The optimization results for all four emitters were experimentally validated in lab-studies. We performed a more exhaustive validation study for the 8.2 lph emitter with an emitter manufacturer. Results from these experiments (which followed ISO standards) showed that the optimized 8.2 lph emitter had a 75% lower activation pressure when compared to the original emitter design.

Poverty affects hundreds of millions of people globally. Market-based strategies can help alleviate poverty in developing countries by encouraging entrepreneurial activity and have the potential to be more effective than traditional approaches, such as development aid from countries or non-governmental organizations. Development organizations often target the agricultural sector because of the prevalence of subsistence and small-scale farming, particularly in rural regions of developing countries. Improving the reliability of irrigation techniques can help farmers expand out of primarily subsistence farming and begin to sell a portion of their crop, thus achieving the objectives of market-based poverty alleviation. Human-powered pumps are a popular tool used in irrigation because they require low capital cost and negligible operating cost. Previous work provided a model for finding Pareto-optimal IDE-style treadle pump designs. This work utilizes that model to produce a dense set of Pareto-optimal designs, and then investigates the robustness of the designs by simulating their performance in a variety of modified use scenarios. Our results show that pumps optimized for low flow rates (less than 3.0 L/s) are highly robust, particularly with respect to age-related changes in the operator’s stature or mobility. In addition, these pumps can operate with near-optimal efficiency across a variety of target flow rates and well depths. These pumps are ideal for single family use or for shared use amongst multiple families in a village. Pumps optimized for flow rates greater than 3.0 L/s are less robust with respect to changes of operator stature (experiencing decreases in flow rate of up to 60%) but may be suitable for use on farms or by service providers.

21st Design for Manufacturing and the Life Cycle Conference: Integrated Product and Process Development

Delayed differentiation enables firms to cost effectively offer a large variation of the same product by using common components until the products need to be differentiated for regional requirements. Using the same components in different product models (component commonality) is the key to enabling delayed differentiation. The objective of this paper is to propose an approach to evaluate the value of component commonality by integrating product design decisions and supply chain decisions. We propose an approach to assess the value of component commonality by simultaneously optimizing product design (i.e., component commonality) and supply chain decisions including supplier selection (replenishment lead time) and inventory policy. The proposed approach is illustrated in motor commonality decisions for electric bicycles. The optimum component commonality and supply chain decisions are investigated under various conditions including different demand variabilities, component costs, inventory tracking costs, and inventory ordering costs.

With recent progress in developing more effective models for representing manufacturing processes, this paper presents an approach towards an open web-based repository for storing manufacturing process information. The repository is envisioned to include several new use cases in the context of information use in smart manufacturing. This paper examines several key benefits through usage scenarios engaging existing engineering activities. Based on the scenarios, the desired characteristics of an open web-based repository are presented, namely that it will be (1) complementary to existing practices, (2) open and net-centric, (3) able to enforce model consistency, (4) modular (5) extensible, and (5) able to govern contributions. A repository will support and motivate the ubiquitous and extended use of standardized representations of unit manufacturing processes in order to promote consistency of performance assessments across industries and provide a tangible, data-driven perspective for analysis-related activities. Furthermore, the paper presents additional benefits and possible applications that could result from a shared manufacturing repository.

Stamping is a core manufacturing process in automobile industry. The measurement of the press force in stamping process has been the major focus of the research in this area. However, it has been established that the press force is itself an integral of the pressure distribution over the contact surfaces. Also full potential of servo-control stamping machines has not yet been achieved due to lack of appropriate sensing functions. In this study, an effort has been made to monitor the stamping process with the help of ultrasonic waves. The waves are employed to obtain the contact conditions between the work piece and the die. It has been shown that the waveforms are affected not only by the workpiece material and its thickness but also by the angle of inclination. Results show that the reflected waves, being more sensitive than transmitted waves, are influenced by the change in work piece related parameters.

A novel concept named sustainable development has come to be considered important to prevent the intensification of climate change. Thus, there have been many reports about the environmental load of consumer goods such as home electronics, car electronics, and so on. However, there have been few reports about the environmental load of industrial goods. Specifically, there has been no report dealing with the environmental load of compact five-axis controlled machine tools and how to use linear-axes and rotational-axes in them.

Then, a suitable end-milling process to reduce the power consumption with a compact five-axis controlled machine tool equipped with an automatic idling reduction servo system by using the developed formula have been discussed. As a result, the combined method of downsizing the machine tool, using suitable control axes, and equipping the machine tool with an automatic idling reduction servo system is found to be effective in reducing the power consumption in a manufacturing process.

Recent growing interest in reducing greenhouse gas (GHG) emissions requires the application of effective energy solutions, such as the utilization of renewable resources. Biomass represents a promising renewable resource for bioenergy, since it has the potential to reduce GHG emissions from various industry sectors. In spite of the potential benefits, biomass is limited due to logistical challenges of collection and transport to bio-refineries. This study proposes a forest biomass-to-bio-oil mixed supply chain network to reduce the GHG emissions compared to a conventional bioenergy supply chain. The mixed supply chain includes mixed-mode bio-refineries and mixed-pathway transportation. Life cycle assessment is conducted for a case study in the Pacific Northwest with the assistance of available life cycle inventory data for biomass-to-bio-oil supply chain. Impact assessment, on a global warming potential (GWP) basis, is conducted with the assistance of databases within SimaPro 8 software. Sensitivity analysis for the case investigated indicates that using the mixed supply chain can reduce GHG emissions by 2–5% compared to the traditional supply chain.

Despite recent efforts to alleviate the electronic waste (e-waste) problem, the product recovery management programs have not reached their full potential. The incompetency of the current e-waste recovery system mostly originates from the collection phase, where the consumers often have the tendency to keep their used electronics in storage rather than returning them. This may be due to lack of awareness about e-waste collection sites or the inconvenience of current e-waste collection infrastructure. To facilitate the collection of unwanted products, ‘e-waste collection’ events have been introduced for convenient and on-time disposal of electronic devices. However, factors such as consumer awareness, the volume of available e-waste for recovery, the resulting economic, environmental and social outcomes of those events, convenience of the location, laws and restrictions on the disposal of certain electronics, and the cost of holding such events make the scheduling a complicated decision, particularly for the remanufacturing industry. The purpose of this study is to characterize the e-waste collection scheduling problem and help decision makers determine the best schedule and characteristics of the e-waste event. An optimization framework has been developed to maximize the amount of collected e-waste.

Rapid innovations in technology lead customers to frequently upgrade to new products. Their current products, now obsolete in terms of technology, aesthetic features and performance, leave behind an ecological footprint that is harmful to the environment. Product take-back systems and remanufacturing methods that promise to minimize the environmental impact are gaining attention among researchers and practitioners in the manufacturing field. A common objective is to find the best option for end of lifecycle (EOL) decisions on whether a product and the components comprising it should be reused, recycled, remanufactured, or disposed. These decisions must entail proper analysis while taking into account customer preferences, which can vary considerably from customer to customer. Mass customization, considered a plausible solution for this problem, is not viable model for many products. In this paper, therefore, we approach this problem from a preference aggregation perspective, particularly, the benevolent dictator model. Using this understanding of aggregated preferences, we address the take-back and possible remanufacturing of products. Once collected, it is questioned whether efficiency enhancing new technology or features should be added in take-back products to improve its performance or add any value. If that is the case, will these remanufactured products, with new technology or features, help in cost-effectively reducing the lifecycle environmental impact of the product, compared to a remanufactured product with original specifications? A home HVAC system was selected to exemplify the design and reuse problem, and show the benefit of favoring environmentally conscious customers in lifecycle decision making.

Ensuring that global infrastructure keeps pace with the demands of economic growth and human wellbeing is anticipated to result in spend of US$57 trillion (2013–30). Specific to the UK power sector, redesigning the electrical transmission network to support decarbonisation of the economy will result in an estimated spend in the region of US$50 billion (2010–20). The challenge within the infrastructure sector is in ensuring that investment productivity is maximized and the appropriate assets are built. One approach being used are decision support tools (DSTs) aimed at assisting the optimum asset choice, by considering a range of costs across the life of the asset. However, there is a gap in ensuring the sustainability of these tools: that is, ensuring that after adoption they continue to offer the same value. The research presented in this paper considers ‘performance decay’ of DSTs and proposes an approach to ensure they remain ‘fit for purpose’. Our research proposes that adopting a quality management system approach will combat performance decay, and move current DSTs from ‘static’ to ‘live’ and evolving states. Within this paper a review of literature is provided. Scenarios are used to explore possible changes in performance, and an industry exemplar used to demonstrate the plausibility of performance decay. An approach to address performance decay by embedding quality management systems techniques is then introduced.

Reverse logistics is an area that has come under increased scrutiny in recent years as legislators and companies try to increase the amount of goods that businesses reuse and recycle. The vehicle routing problem with simultaneous pickup and delivery arises when firms want to reduce handling costs by dealing with deliveries and returns in one operation. This is a complex problem for planners who aim to minimise the vehicle route length as the vehicle load rises and falls during a tour of facilities. This paper investigates the use of Ant Colony Optimisation to find solutions to this problem. An algorithm combining elements of three different studies is proposed. The algorithm finds results within 0.2% of the best known results and performs well for half of the benchmark problems, but needs further work to reach the same level on the other half. It is found that the proposed changes can have up to a 3.1% improvement in results when compared to previous methods run on this algorithm.

Solid end mills with multi-section cutting edges and variable helix angles are available for application. New types of solid end mills for low energy consumption have recently been developed. These so-called Low Power Cutting (LPC)-Tools are characterized by differential helix angles.

Compared to solid end mills with variable helix angles, the new differential helix angles change their pitch continuously over the cutting edge length. Due to this fact the cutting conditions are not in a constant state during the revolution of the cutting tool. Existing mathematical approaches for the calculation of cutting forces only consider constant helix angles in milling operations.

This paper describes an approach for the prediction of cutting forces for differential helix angles. The developed mathematical model is based on geometrical considerations. Due to a continuously changing pitch over the cutting edge length a numerical approach for the mathematical model is chosen.

Driven by the high importance of resource efficiency the importance of lightweight construction rises across all industries. Due to their high lightweight construction potential, carbon fiber reinforced plastics (CFRP) are increasingly applied.

But CFRP manufacturing often is an expensive small-scale production where it is necessary to use removable mold cores to manufacture hollow parts. To improve the complex process of mold core making future process chains including additive manufacturing (i.e. 3D-printing) can be used.

This paper provides a proposal for a hollow CFRP reference part based on facts taken directly from the field of application. The reference part will allow the evaluation of economic and ecological impact of process chains in mold core making for the CFRP production. Therefore, representative parts from different industry sectors were selected and analytically examined regarding their geometrical features. Based on a scientific recomposition of these features the reference part was developed.

In the last few years, the environmental problem has become a very serious issue and the research world has answered to this growing interest with the development of a high number of ecodesign methods and tools. However their use in real contexts is still quite low, due to their complexity, time consuming and need for specific knowledge. From this reason, it comes the need for tools that support designers in the implementation of ecodesign strategies.

This paper presents the Case Based Reasoning (CBR) methodology and tool, which represents the knowledge and the best practices for manufacturing products. It supports designers in the re-design process of products, by allowing them to gradually acquire knowledge and to solve problems in a rapid and simplified way, through the collection and sharing of ecodesign knowledge in a structured Database. The tool has been tested into two industrial companies to verify its potentialities, evaluate its usability and identify its limits.

A growing interest toward sustainability actions at every level is characterizing the industrial sector. Following the environmental trend, further developments and improvements regarding the sustainability assessment of manufacturing processes is needed. With a particular focus on machining processes, the optimization of working parameters can represent a valid step forward in sustainable manufacturing.

This paper aims to provide companies with the needed tool to independently asses the environmental performance of their customized machining operations. The purpose of the presented work is then to demonstrate that energy consumption calculated with empirical mathematical models available in literature, gives the greatest contribution to the environmental impact for a selection of machining processes by means of Life Cycle Assessment (LCA). Such objective lead to a clear need of specific models for the calculation of environmental impact of machining processes instead of available LCA datasets. Available mathematical models are adopted to provide a realistic energy consumption profile by using processing time variables. Such values are calculated through 3D models whose are used to recognize the needed manufacturing operations together with relative processing times.

In order to validate the previous assumption, a couple of machining processes have been selected as reference and analyzed by setting up a detailed Life Cycle Inventory (LCI) model. Results shown that among the different inputs and outputs, the energy consumption carries the highest impact. Over 90% of the total impact for the chosen impact categories (Global Warming Potential and Eco-Costs) is attributable to the energy consumption meaning that, for the sake of simplification, the environmental profile of such operations is overlapped by its energy consumption.

Nowadays, the energy factor is growing in importance because it really affects the production costs of factories. Not least, energy regulation and rules are stricter as time goes by. Tools for energy monitoring are needed.

The objective of this work is to propose a tool aimed to assess the energy value flowing in a production system, according to the Energy Value Stream Mapping method (EVSM). The final goal is to increase the sustainability of the processes through identification of energy wastes, aiming at eliminating them. The tool is a lean graphic instrument; it permits to represent explicitly the energy flows along the process with respect to the concept of value creation.

The tool permits to clearly identify and quantify different types of energy flows concerning different carriers, even in a complex production plant where multiple energy carriers act at the same time. Finally, it will be shown the testing of the tool in a household appliances production system.

Life cycle analysis (LCA) is often used to compare the sustainability of product concepts, but how our intuition factors into analysis and innovation is rarely considered. Intuitive evaluations of sustainability by designers not only help scope LCAs, but can be a dominant force in early stage decision making and concept creation. To elicit intuitive responses from experts, eight environmentally conscious products were presented to 22 designers as part of a professional workshop survey. Participants rated their own experience in design and sustainability and rated the sustainability and innovativeness of each product from low to high. These ratings are compared with available LCAs and environmental impact information. The results indicate that self-perceived expertise in sustainability does not significantly change perception of the sustainability of products and that sustainability ratings and innovativeness ratings were moderately correlated.

Product disassembly is critical to remanufacturing, reuse, and recycling activities, which are essential reverse flows in circular economy systems. While disassembly optimization and analysis methods are well developed, many of these methods rely on basic disassembly information, i.e. a precedence matrix. Obtaining this basic disassembly information is a non-trivial task that has, in the past, required extensive manual analysis of a product assembly. This process can be significantly improved by developing methods capable of automatically extracting disassembly data from CAD/CAM assemblies. If this can be achieved accurately, efficiently, and in a timely manner, numerous disassembly optimization methods will become readily available to product designers. In pursuit of this aim, the objective of this research is to automatically extract part contact information from assembly models and determine disassembly operation feasibility. The described method uses CAD assembly STEP data as input, extracts disassembly relevant information such as, component surface contour type, Cartesian points, and normal direction of the surface, and determines the feasibility of disassembly of each component which is represented as a precedence matrix. Disassembly feasibility is evaluated using the contact function. A case assembly was tested and the precedence matrix was successfully determined for part contact disassembly constraints. Results of the research indicate that the method is capable of extracting disassembly data from CAD STEP assemblies but has critical limitations that must be overcome to be influential during design.

As more companies and researchers become interested in understanding the relationship between product design decisions and eventual environmental impact, proposed methods have explored meeting this demand. However, there are currently limited methods available for use in the early design phase to help quantify the environmental impact of making design decisions. Current methods, primarily vetted Life Cycle Assessment (LCA) methods, require the designer to wait until later in the design phase, when a product’s design is more defined; alternatively, designers are resigned to relying on prior sustainable design experience and empirical knowledge. There is a clear need to develop methods that quantitatively inform designers of the environmental impact of design decisions during the early design phase (particularly during concept generation), as this allows for reexamination of decisions before they become costly or time-intensive to change. The current work builds on previous research involving the development of a search tree of sustainable design knowledge, which, applied during the early design phase, helps designers hone in on the impact of product design decisions. To assist in quantifying the impact of these design decisions, the current work explores the development of a weighting system associated with each potential design decision. The work presented in this paper aims to quantify the general environmental impact potential design decisions have on a consumer product, by using a multi-layer perceptron neural network with back propagation training — a method of machine learning — to relate the life-cycle assessment impact of 37 case study products to product attributes. By defining the relationship between LCA data and product attributes, designers in the early design phase will be more informed of which product attributes have the largest environmental impact, such that the designer can redesign the product to have reduce this impact.

Bio-inspired design, or biomimicry, is an approach to innovation that takes nature’s time-tested patterns, forms, functions, processes, and materials and uses them to develop engineering solutions. In this project we take inspiration from biological morphologies to develop new forms for semi-recyclable products. Biological systems exhibit multi-functionality from form, not necessarily material, which offers inspiration for product life-cycle management. The goal is to better understand the connection between form and function as found in nature to enable sustainable product design and enhance additive manufacturing processes. Through the application of bio-inspired design product recyclability is increased through minimization of material diversity while still achieving desired functions. One inspiring biological morphology that has been utilized across multiple biological kingdoms and in this research is variations in hardness and flexibility found in alternating layers that are used to provide strength, durability and protection. Another inspiring morphology considered in this research is the backbone of water-diving birds, which consists of an intricately braided spine with parallel holes along each side. These holes allow for shock absorbance and force dispersion. These multi-function forms have resulted in the redesign of a semi-recyclable product fabricated using additive manufacturing to create a product that is made from a single material yet still achieves all necessary functions. Key contributions of this research include approaches for additive manufacturing strategies such as material utilization that align with a product’s life cycle, thus increasing the recyclability of the product.

In this paper, we present the methodology and results from the evaluation of shapeSIFT: a multi-dimensional visualization tool for exploring part repositories in the context of eco-conscious design. The goals of this work are twofold, to create a categorization scheme for user evaluation methods specific to information visualization tools in eco-conscious design, and to formulate a user study for the shapeSIFT interface based on the developed categorization scheme. Our proposed scheme categorizes user evaluation methods based on two axes, (a) focus of the conducted user evaluation, and (b) development stage of the tool being evaluated. This scheme was used as the basis for conducting a guided formative expert review of the shapeSIFT interface with five domain experts in an industry setting. In this study we gathered experts’ comments, feedback, and usage behaviors, while they used the shapeSIFT interface to perform tasks reflective of real-world design practices. Results from the study show that shapeSIFT was easy-to-use and effective for part selection in the context of redesign. Motivated by these positive preliminary results, we intend to improve the shapeSIFT interface and conduct follow-up studies with a larger pool of designers.

Eco-Industrial Parks (EIPs) and Industrial Symbioses (IS) have provided several cost-effective and environmental friendly solutions for the economic growth of countries. The need for excessive materials, water and energy can be reduced by exchanging wastes, by-products and energy among different clusters of industries, which is the fundamental goal of establishing synergies among industries. Symbioses design looks for the best set of connections among industries to satisfy defined objectives. However, there are not enough data to support the design of a new EIP for some industries. The existing research contains multiple objective optimization methods, but lacks details in the real industrial world to consider comprehensive criteria in design of flow exchanges due to the large cost and long establishment time for those synergies. This paper presents a multi-objective model to decide the best network of industries for several exchanges among them. The model helps minimizing costs for multiple product exchanges while considering environmental impacts to be reduced. Moreover, this paper investigates uncertainties affecting synergies within EIPs by incorporating in a modeling process. The presented models are validated through optimizing symbioses in an EIP. The efficiency of single and multiple objective models is analyzed for effects of the selected uncertainties. Future research directions are also discussed.

Additive manufacturing is a family of processes that has been gaining attention recently by industry, researchers, and policy makers. Many claims have been made about improved sustainability performance over traditional subtractive machining processes. However, these claims have not been substantiated through sustainability characterization. This paper presents a method to compare several sustainability metrics for parts of varying size produced by additive and subtractive manufacturing processes. The production of a part is modeled using direct energy deposition (DED) as the representative additive process and milling as the representative subtractive process. The results indicate that milling has superior performance than DED when relatively small volumes of material are removed from an initial workpiece. As more material is removed by milling, the difference between the performance of DED and milling decreases. With increasing material removal volumes, DED becomes the superior process from a sustainable manufacturing perspective. This research gives decision makers a demonstrated approach and selection windows for the superior process type for a given workpiece design and preselected sustainability assessment metrics.

The bottom up demand from consumers for more sustainable products, and the top down need to comply with government regulations motivates manufacturers to adopt tools and methods to evaluate their operations for opportunities to reduce environmental impact and improve competitiveness. Manufacturers have actively improved the sustainability of their products through the use of such tools and methods. However recently, manufacturers are struggling to maintain the necessary gains in energy and material efficiency due to the assessment inaccuracies of current ad hoc methods and their inability to identify large sustainability improvement opportunities. Overcoming this barrier requires standardized methods and tools that are implementable and which contain accurate manufacturing process-level information. To aid in developing such methods and tools, this study contrasts the perspective of industry and academic research on the topics of sustainable manufacturing metrics and measurements, and process modeling to determine the deficits that exist in enacting academic theory to practice. Furthermore, this study highlights some of the industry responses to the development of related standards for sustainability assessment.

It is beneficial to conduct LCA(Life Cycle Assessment) during early stages of product development, as the earlier the environmental problems associated with the product life cycle are discovered, the less costly and more effective the preventing measures are. However, due to the lack of data communication tools between CAD and LCA systems, life cycle data collection during design stage is difficult. This paper presents a feature-based method of UGNX-LCA integration for sustainable product development. A feature-based multi-view life cycle model for integrating product-process-LCI (Life Cycle Inventory) data is developed based on mapping mechanism between engineering domains of product design, process planning and LCA. Data migration from UGNX models to LCA, including UG modeling feature identification, UG-LC(Life Cycle) feature transformation and LC feature model output are realized by embedded integrator. A case study of data migration from UGNX to LCA is presented to demonstrate the proposed approach.

This paper proposes a FBS-based energy modeling method for highly energy-efficient product design. By this modeling method, the structured relations between the product energy demands and design parameters are developed along with the systematic FBS design process. Meanwhile, by introducing quantified energy analysis to the systematic design domain mapping course, the method helps designers understand energy consuming behaviors of the product and identify efficiency-improving breakthrough points in product design. A case study of a mechanical servo press is presented for the demonstration of this method.

10th International Conference on Micro- and Nanosystems: Bio MEMS/NEMS

The microvascular network is a simple but critical system that is responsible for various important biological mechanisms in the bodies of all animals. The ability to generate a functional microvessel in vitro not only makes it possible to engineer vital tissue of considerable size but also serves as a platform for biomedical studies. In this study, we propose a simple method for fabricating customized cylinder micro-scaffolds for the in vitro development of microvascular networks. By integrating micro-electro-mechanical systems techniques with thermal reflow, we design and fabricate a micro-scale hemi-cylinder photoresist template. Then, a replica mold of polydimethylsiloxane, produced by casting, is used to generate microvascular network scaffolds of poly(lactide-co-glycolide) (PLGA). We selected the human umbilical vein endothelial cell (HUVEC) as our model endothelial cell, seeded it onto both sides of the PLGA scaffold, and cultured it using a traditional approach with no pumping system. Results from fluorescent staining demonstrate that the scaffold was covered with HUVECs and that the desired microvascular network and pattern was generated in vitro. The proposed method enables the culture of cells on a scaffold using a conventional culture approach and allows continuous monitoring of cell conditions. The cell-covered scaffold can serve as a framework for building large tissues, while the formed microvascular network, after degradation of the biodegradable PLGA cylinder, can be used as the core of a vascular chip for in vitro circulation studies.

This paper investigates the design of a nano-injection system that can deliver genetic material to cells within live tissue. The approach to creating such a system was to create candidate designs that meet all the requirements for successful in vivo injection and can be fabricated using silicon etching. The designs were tested through large-scale prototyping and through models that describe the systems’ behavior on the micrometer scale. One design consists of an array of lances on a rigid backing. The other design consists of an array of lances grouped in sets of three on a backing that can conform to the shape of the tissue being injected. Each design was prototyped in 3D printed ABS plastic. Preliminary results were qualitative and showed that the rigid and flexible designs performed similarly on mostly flat and irregular surfaces. On convex surfaces with a strong curvature (radius of curvature of about 2 cm), the flexible array gave slightly better results. Final testing gave a quantitative comparison of the two designs’ efficiencies on strongly curved convex surfaces. These results supported the preliminary results that the flexible array is more efficient in reaching points on the tissue than the rigid array is. As the applied force increased, each array performed more efficiently.

10th International Conference on Micro- and Nanosystems: Characterization and Measurement of Micro and Nano Device and Materials

In this study, nano-sepiolite (NSP) was synthesized, dispersed and used as a replacement for regular additives in water-based drilling fluids to enhance its lubricity. Due to its structure and morphology the suspended sepiolite nanoparticles is expected to enhance the stability against segmentation along with better thermal, mechanical and electrical properties. The morphology of the nano-modified drilling fluids and the dispersion of the nano-sepiolite are characterized using XRD and SEM. The influences of various sizes and compositions of the NSP on the stability of drilling fluids on HTHP conditions are investigated. Results revealed that the drilling fluids lubricity and the drillstring axial force transfer were significantly improved by using NSP in the base drilling fluids. The investigations showed that the lubricity and rheological properties of the nano-modified drilling fluids depend on the size and composition of the NSP additive. The studies were performed on normal and HTHP conditions.

Electrical Impedance Measurement of PZT Nanofiber sensors are performed and material properties including resistivity and dielectric constant are derived from the measurements. Nanofibers formed by electro-spinning with diameters ranging from 10 to 150 nm were collected and integrated into sensors using microfabrication techniques. The nanosensor impedance was extremely high at low frequencies and special matching circuitry was fabricated to detect output. The resulting impedance measurements are also compared with those of individual nanofibers that were tested using Scanning Conductive Microscopy (SCM) and Conductive AFM.

10th International Conference on Micro- and Nanosystems: Dynamics of MEMS and NEMS (MNS/VIB/MSNDC)

The axisymmetric snap-through of an initially curved circular micro plate, subjected to a transversal distributed electrostatic force is studied. The analysis is based on a reduced order (RO) model resulting from the Galerkin decomposition, with buckling modes of a flat plate used as the base functions. In order to check the validity of the RO model, the corresponding problem for a displacement-independent (“mechanical”) load is solved, and a comparison between the RO model and those obtained using finite elements (FE) analysis is carried out. It is shown, that the two are in good agreement, indicating that the RO model can be used for a plate undergoing electrostatic loading. However, the study shows that at least three degrees of freedom (DOF) are required for an accurate prediction of the equilibrium path and bistability. The coupled electromechanical analysis shows that due to the nonlinearity of the electrostatic load, the snap-through occurs at a lower displacement than in the case of the “mechanical” load. Moreover, the study concludes that actuation of plates of realistic dimensions can be achieved by reasonably low voltages.

The objective of this paper is to demonstrate the integration of a MOF thin film on electrostatically actuated microstructures to realize a switch triggered by gas and a sensing algorithm based on amplitude tracking. The devices are based on the nonlinear response of micromachined clamped-clamped beams. The microbeams are coated with a Metal-Organic Framework (MOF), namely HKUST-1 to achieve high sensitivity. The softening and hardening nonlinear behaviors of the microbeams are exploited to demonstrate the ideas. For gas sensing, an amplitude-based tracking algorithm is developed to quantify the captured quantity of gas. Then, a MEMS switch triggered by gas using the nonlinear response of the microbeam is demonstrated. Noise analysis is conducted, which shows that the switch has high stability against thermal noise. The proposed switch is promising for delivering binary sensing information, and also can be used directly to activate useful functionalities, such as alarming.

This paper demonstrates experimentally, theoretically, and numerically a wide-range tunability of an in-plane clamped-clamped microbeam, bridge, and resonator compressed by a force due to electrothermal actuation. We demonstrate that a single resonator can be operated at a wide range of frequencies. The microbeam is actuated electrothermally, by passing a DC current through it. We show that when increasing the electrothermal voltage, the compressive stress inside the microbeam increases, which leads eventually to its buckling. Before buckling, the fundamental frequency decreases until it drops to very low values, almost to zero. After buckling, the fundamental frequency increases, which is shown to be as high as twice the original resonance frequency. Analytical results based on the Galerkin discretization of the Euler Bernoulli beam theory are generated and compared to the experimental data and to simulation results of a multi-physics finite-element model. A good agreement is found among all the results.

In this work, we present a novel device developed by integration of an array of Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) with a microfluidic chip that can be used for characterizing the acoustical properties of the liquid present in the back-cavity of the PMUT. PMUT membrane operates in flexural mode of vibration and it is directly coupled with the cylindrical back-cavity formed during the release of the PMUT membrane. This leads to very strong structural-acoustic coupling between the PMUT and the liquid present in the its back-cavity. Presence of fluid around the thin PMUT membrane causes a significant reduction in the resonant frequencies of the PMUT due to mass loading imposed by the surrounding fluid. It also leads to the excitation of the acoustic modes of the cylindrical back-cavity when the PMUT vibrates near the fundamental acoustic frequencies of the cavity. These acoustic reverberations appear in the vibration response of the PMUT in form of additional resonant peaks. Further we explore the feasibility of capturing the acoustic signature of microbubbles introduced in the back-cavity liquid. Microbubbles are generated on the microfluidic chip using flow focusing technique and introduced in the cylindrical back-cavity of the PMUT through a network of channels and wells made on PDMS and adhered to the PMUT from the backside. This approach can provide an alternative method for on-chip characterization of microbubbles.

In micro/nanometer scale mechanical resonators, constructive utilization of intentional nonlinearity has suggested ways to leverage beneficial nonlinear characteristics in their design for various applications. Previous studies have also shown that the geometric nonlinearity is effectively implemented and tailored through integration of nonlinear couplings to an otherwise linear microcantilever. Here, we demonstrate experimentally a nonlinear micromechanical resonator consisting of a silicon microcantilever axially constrained by a polymer attachment exhibiting a strong nonlinear hardening behavior not only in its first flexural mode but also in higher modes. A theoretical model representing the system with geometrically nonlinear stiffness and damping is analyzed by the method of multiple scales, which is favorably validated by good agreement with experimentally obtained nonlinear responses.

Bacteriophage T4 is one of the most common and complex of the tailed viruses that infect host bacteria using an intriguing contractile tail assembly. Despite extensive progress in resolving the structure of T4, the dynamics of the injection machinery remains largely unknown. This paper contributes a first model of the injection machinery that is driven by elastic energy stored in a structure known as the sheath. The sheath is composed of helical strands of protein that suddenly collapse from an energetic, extended conformation prior to infection to a relaxed, contracted conformation during infection. We employ Kirchhoff rod theory to simulate the nonlinear dynamics of a single protein strand coupled to a model for the remainder of the virus, including the coupled translation and rotation of the head (capsid), neck and tail tube. Doing so provides an important building block towards the future goal of modeling the entire sheath structure which is composed of six interacting helical protein strands. The resulting numerical model exposes fundamental features of the injection machinery including the time scale and energetics of the infection process, the nonlinear conformational change experienced by the sheath, and the contribution of hydrodynamic drag on the head (capsid).

More and more systems exploit parametric excitation (PE) to improve their performance compared to conventional system. Especially in the field of micro-electromechanical systems (MEMS) such technologies rapidly gain in importance. Different to conventional resonance cases PE may destabilise the system’s rest position when parametrically excited time-periodically with a certain PE frequency. At such parametric resonances vibrations are only limited due to non-linearities. The system is repelled by the unstable rest position and enters a bifurcated limit cycle.

Finding these limit cycles has become more easy in recent years. Advances have been made in numerical path following tools regarding both their power and their user friendliness. As a result, designing such systems has become more common. Indeed, the focus of studies has been on 1DOF systems mostly.

However, for multi degree of freedom systems choosing a meaningful phase space to discuss the results is a task on its own. Quasi-modally transforming the equations of motion, the vibrations are decomposed allowing one to focus on the predominant modes. By concentrating on these predominant modes, continuation results can be displayed in meaningfully reduced phase-parameter spaces. Basins of attraction can be found in Poincaré sections of these phase-parameter spaces.

Employing these approaches, it is demonstrated how to investigate a non-linear 2DOF PE MEMS, how to change the characteristics of the limit cycles and how this affects their basins of attraction.

In present paper, resonant characteristics of vibrating micro/nano-beams are investigated based on the nonlocal elasticity theory. The natural frequency and quality factor of the micro/nano-beams are known as important resonant characteristics which play crucial roles in resonant vibration of the beams in air environments. To determine the resonant characterizes of the micro/nano-beams, the governing vibration equation of the nonlocal beam with fixed end supports is derived considering the air damping force. As the beam is modeled the beam as a string of vibrating adjacent spheres in interaction with the ambient air environment, the air damping force is obtained as a function of the resonant frequency. Furthermore, to calculate the quality factor of the size-dependent micro/nano-beams, the time-dependent vibration equation is presented in modal space based on the orthogonality conditions. Therefore, the quality factor obtained as a function of the natural frequencies and size-dependent nonlocal parameter at various resonant modes of vibration. Then, a parametric study investigates the nonlocal effects on the quality factor of the resonant micro/nano-beam. The obtained results indicate that the nonlocal size effects decreases the quality factor. In addition, the size effects play more prominent role at the higher resonant modes of vibration.

10th International Conference on Micro- and Nanosystems: Emerging Topics in Micro and Nanosystems

Ortho-planar springs are characterized by their planar shape and the dominant out of plane motion. These springs have benefits for integration in piezoelectric energy harvesting transducers, because of their compactness and monolithic planar manufacturing. The operating behavior in the first low frequency bending mode can be optimized by obtaining an appropriate strain distribution. A holistic design approach is proposed that contains both the focus on strain distribution as on the low frequency dynamic operation challenge. Therefore a classification based on the strain distribution has been made, which is derived from the perspective of loading, clamping and geometry of single flexures of ortho-planar springs. A comparison based on the type of strain (bending/torsion ratio), strain inversion,off-axis stiffness and the natural frequency-normalized area factor (NFNA) has been performed. The double clamped folded configuration shows the most potential for future optimal low frequency transducer designs.

Nonlinear forced vibration of the nonlocal curved carbon nanotubes is investigated. The governing equation of vibration of a nonlocal curved carbon nanotube is developed. The nonlinear Winkler and Pasternak type foundations are chosen for the nanotube resonator system. Furthermore, the shape of the carbon nanotube system is assumed to be of a sinusoidal curvature form and different types of the boundary conditions are postulated for the targeted system. The Euler-Bernoulli beam theory in conjunction with the Eringen theory are implemented to obtain the partial differential equation of the system. The Galerkin method is applied to obtain the nonlinear ordinary differential equations of the system. For the sake of obtaining the primary resonance of the considered system the multiple time scales method is utilized. The influences of different parameters, namely, the position of the applied force, different forms of boundary condition, amplitude of curvature, and the coefficient of the Pasternak foundation, on the frequency response of the system were fully investigated.

This paper deals with amplitude-frequency response of electrostatic nanotube nanotweezer device system. Soft alternating current (AC) of frequency near natural frequency actuates the nanotubes. This leads the system into parametric resonance. The Method of Multiple Scales (MMS) in which the nonlinear electrostatic and van der Waals forces are expanded in Taylor series is used to compare two expansions, one up to third power and the other up to fifth power. The frequency response of the system is reported and the effects of van der Waals forces, electrostatic forces, and damping forces on the frequency response are investigated.

Rapid mixing in microchannels plays a significant role in chemical, biological and medical analysis fields. Microchannels are widely used for chemical and biochemical reactions because of their high surface to volume ratio. However, the rate of mixing of two or more chemical reagents is less as the flow through the micro-channel is highly laminar. Thus, two reactive fluids are predominantly parallel when they flow along the length of the channel. Generally, obstacles or surface modifications are made in the flow path which induces chaotic advection in the fluids. Considerable amount of research has been done in the past in developing different types of surface modifications to enhance the chaotic mixing. But, the intricate nature of fluid flow phenomenon makes it difficult to design the surface modification suitable to achieve the maximum rapid mixing. The present work aims at designing micromixers with the objective of obtaining rapid mixing with reduced pressure drop. A topology optimization algorithm is illustrated in the present manuscript for the design of optimal micromixer configuration. Finite element based optimization for surface modification of micromixer is developed using porosity of the channel as the control variable. In the present work, the optimisation solver works over two objectives. One is to increase the mixing in the channel and the second is to reduce the pressure drop. Numerical experiments are done to test the algorithm to obtain the optimal surface modification to achieve maximum rapid mixing between the two fluids. The results show that rapid mixing is achieved with the modified topology obtained using the code.

We present a MEMS microphone that converts the mechanical motion of a diaphragm, generated by acoustic waves, to an electrical output voltage by capacitive fingers. The sensitivity of a microphone is one of the most important properties of its design. The sensitivity is proportional to the applied bias voltage. However, it is limited by the pull-in voltage, which causes the parallel plates to collapse and prevents the device from functioning properly. The presented MEMS microphone is biased by repulsive force instead of attractive force to avoid pull-in instability. A unit module of the repulsive force sensor consists of a grounded moving finger directly above a grounded fixed finger placed between two horizontally seperated voltage fixed fingers. The moving finger experiences an asymmetric electrostatic field that generates repulsive force that pushes it away from the substrate. Because of the repulsive nature of the force, the applied voltage can be increased for better sensitivity without the risk of pull-in failure. To date, the repulsive force has been used to engage a MEMS actuator such as a micro-mirror, but we now apply it for a capacitive sensor. Using the repulsive force can revolutionize capacitive sensors in many applications because they will achieve better sensitivity. Our simulations show that the repulsive force allows us to improve the sensitivity by increasing the bias voltage. The applied voltage and the back volume of a standard microphone have stiffening effects that significantly reduce its sensitivity. We find that proper design of the back volume and capacitive fingers yield promising results without pull-in instability.

10th International Conference on Micro- and Nanosystems: Micro and Nanomanufacturing

Assembly sequence planning is an engineering problem that has been of great interest in the manufacturing field at the macro-scale. As more complex assemblies are desired at the micro and nano scales it is no longer feasible for human beings to plan and execute the production of these systems. A promising algorithm that allows optimization of assembly sequence plans that has been developed is called the Breakout Local Search. One drawback of this algorithm is its inability to consider the need for intermediate sub-assemblies to generate feasible solutions. Here an expansion to the BLS algorithm, called the Sub Assembly Generating BLS (SABLS) algorithm, is proposed. The fitness function of this new algorithm is also tailored to the specific constraints and motion primitives for a micromanipulation test-bed allowing for its use in microassembly applications. It is shown that the proposed algorithm is capable of generating optimized solutions that can be assembled with this limited degree of freedom system.

Due to its ability to produce low-cost and high repeatable micro polymeric parts, injection moulding of micro components is emerging as one of the most promising enabling technologies for the manufacturing of polymeric micro-parts in in many different fields, from IT to Healthcare, to Medicine.

However, when approaching the micro-scale, different issues related to the process should be addressed, especially as the depth of the mould cavity becomes very thin. In particular, the mould roughness could affect the surface quality of the produced micro components, like in macro moulding, as well as the complete filling of the parts. Although micro-injection moulding process has been extensively studied, further research on the effect of mould roughness conditions and on non-Newtonian fluid flow in micro-cavities are required. This will shed a light and open up new paths for a deeper understanding of the moulding scenario.

The main objective of the present paper is the evaluation of the influence of the mould roughness on the polymer flow during micro injection moulding process. The test parts have been realized in POM material and have thickness lower than 250 μm. The test part design has been properly conceived in order to neglect the effect of dimensions and geometry and to highlight the roughness contribution during the filling phase of micro injection moulding process. The experimentation has been performed considering cavities with different roughness values (3 levels) and decreasing depths (3 levels), for a total of nine test parts manufactured by micro-electrical discharge machining process (μ-EDM).

The results of the experiments are discussed in the paper and show that cavity surface roughness affects the injection process as the moulding scale level is decreased. In particular, when the cavity depths are reduced, higher surface roughness promotes the filling of components and this finding could be ascribed to the increase of wall slip effect.

In the present paper, a numerical approach to model the layer-by-layer construction of cured material during the Additive Manufacturing (AM) process is proposed. The method is developed by a recursive mechanical finite element (FE) analysis and takes into account forces and pressures acting on the cured material during the process, in order to simulate the behavior and investigate the failure condition sources, which lead to defects in the final part geometry. The study is focused on the evaluation of the process capability Stereolithography (SLA), to build parts with challenging features in meso-micro scale without supports. Two test cases, a cantilever part and a bridge shape component, have been considered in order to evaluate the potentiality of the approach. Numerical models have been tuned by experimental test. The simulations are validated considering two test cases and briefly compared to the printed samples. Results show the potential of the approach adopted but also the difficulties on simulation settings.

This paper reports research in fabrication of cylindrical stents using carbon-infiltrated carbon nanotubes (CI-CNT), a material with good hemocompatibility. We demonstrate growth of CI-CNT forests in patterned lines on a 3 mm diameter stainless steel (SS) rod. Lines were patterned parallel, at 7°, at 45°, and perpendicular relative to the axis of the rod. Minimal cracking was seen in the parallel and angled lines. Significant cracking was seen in the perpendicular lines and we attempted to characterize the cracking in order to correlate it to width of the lines and height of the forest. No correlation was found but the average uncracked length was determined to be 414 μm with a standard deviation of 67 μm. We also demonstrate successful growth with minimal cracking of CI-CNT forests in a zig-zag type pattern in an effort to further the possibility of creating a coronary stent utilizing CI-CNT. Some of the patterned samples were also removed from the cylindrical substrate, resulting in free-standing, patterned, cylindrical patterns made from CI-CNT.

10th International Conference on Micro- and Nanosystems: Micro Mechanics and Surface Engineering of Artificial and Biological Materials

The periodontal ligament (PDL) is a soft connective tissue which exhibits an inhomogeneous, nonlinear, and anisotropic material properties. and the elastic modulus of different positions on each section are not the same, analysis of the material properties of PDL enables a better understanding of biomechanical features for tooth movement. The aim of this study was to study the elastic modulus of different section of PDL in nanoindentation. Experimental results indicate that the average elastic modulus elastic modulus in midroot are lower than cervical margin and apex, and there is large change in the circumferential regions.

10th International Conference on Micro- and Nanosystems: Micro- and Nanomechanisms and Robotics

A multiple-mode dynamic model is developed for a piezoelectrically-actuated micro-robot with multiple legs. The motion of the micro robot results from dual direction motion of piezoelectric actuators in the legs, while the complexity of micro robot locomotion is increased by impact dynamics. The dynamic model is developed to describe and predict the micro robot motion, in the presence of asymmetrical behavior due to non-ideal fabrication and variable properties of the underlying terrain. The dynamic model considers each robot leg as a continuous structure moving in two directions derived from beam theory with specific boundary condition. Robot body motion is modeled in six degrees of freedom using a rigid body approximation. Individual modes of the resulting multimode robot are treated as second order linear systems. The dynamic model is tested with a meso-scale robot prototype having a similar actuation scheme as micro-robots. In accounting for the interaction between robot and ground, the dynamic model with first two modes of each leg shows good match with experimental results for the mesoscale prototype, in terms of both magnitude and the trends of robot locomotion with respect to actuation conditions.

Precise control of micro-level objects for cell sorting, cell manipulation, targeted drug delivery etc., still remains a great challenge in the field of bio-medical engineering. Even though magnetic actuation has emerged as a harmless, high actuation speed, low cost alternative for micromanipulation of cells, the throughput of the system is still low because of the undesirable effects of flow fields and sensing noise. This paper reports a parametrized feedback policy to perform non-prehensile magnetic micromanipulation [9] to push the target cell to specified goal location under the influence of flow field, sensing noise, drag and magnetic forces. We report an optimization based parameter tuning approach to reduce the transport time using developed feedback policy. Flow field and image processing noise present in the system was experimentally measured and incorporated into the developed models and simulations. We have tuned the feedback policy parameter to reduce the travel time via simulation experiments.

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